GloPro: Globally-Consistent Uncertainty-Aware 3D Human Pose Estimation & Tracking in the Wild

An accurate and uncertainty-aware 3D human body pose estimation is key to enabling truly safe but efficient human-robot interactions. Current uncertainty-aware methods in 3D human pose estimation are limited to predicting the uncertainty of the body posture, while effectively neglecting the body shape and root pose. In this work, we present GloPro, which to the best of our knowledge the first framework to predict an uncertainty distribution of a 3D body mesh including its shape, pose, and root pose, by efficiently fusing visual clues with a learned motion model. We demonstrate that it vastly outperforms state-of-the-art methods in terms of human trajectory accuracy in a world coordinate system (even in the presence of severe occlusions), yields consistent uncertainty distributions, and can run in real-time.Comment: IEEE International Conference on Intelligent Robots and Systems (IROS) 202

BodySLAM: Joint Camera Localisation, Mapping, and Human Motion Tracking

Estimating human motion from video is an active research area due to its many potential applications. Most state-of-the-art methods predict human shape and posture estimates for individual images and do not leverage the temporal information available in video. Many "in the wild" sequences of human motion are captured by a moving camera, which adds the complication of conflated camera and human motion to the estimation. We therefore present BodySLAM, a monocular SLAM system that jointly estimates the position, shape, and posture of human bodies, as well as the camera trajectory. We also introduce a novel human motion model to constrain sequential body postures and observe the scale of the scene. Through a series of experiments on video sequences of human motion captured by a moving monocular camera, we demonstrate that BodySLAM improves estimates of all human body parameters and camera poses when compared to estimating these separately.Comment: ECCV 2022. Video: https://youtu.be/0-SL3VeWEv

BodySLAM++: Fast and Tightly-Coupled Visual-Inertial Camera and Human Motion Tracking

Robust, fast, and accurate human state - 6D pose and posture - estimation remains a challenging problem. For real-world applications, the ability to estimate the human state in real-time is highly desirable. In this paper, we present BodySLAM++, a fast, efficient, and accurate human and camera state estimation framework relying on visual-inertial data. BodySLAM++ extends an existing visual-inertial state estimation framework, OKVIS2, to solve the dual task of estimating camera and human states simultaneously. Our system improves the accuracy of both human and camera state estimation with respect to baseline methods by 26% and 12%, respectively, and achieves real-time performance at 15+ frames per second on an Intel i7-model CPU. Experiments were conducted on a custom dataset containing both ground truth human and camera poses collected with an indoor motion tracking system.Comment: IROS 2023. Video: https://youtu.be/UcutiHQwbG

Evidence for the importance of resonance scattering in X-ray emission line profiles of the O star ζ\zeta Puppis

We fit the Doppler profiles of the He-like triplet complexes of \ion{O}{7} and \ion{N}{6} in the X-ray spectrum of the O star $\zeta$ Puppis, using XMM-Newton RGS data collected over $\sim 400$ ks of exposure. We find that they cannot be well fit if the resonance and intercombination lines are constrained to have the same profile shape. However, a significantly better fit is achieved with a model incorporating the effects of resonance scattering, which causes the resonance line to become more symmetric than the intercombination line for a given characteristic continuum optical depth $\tau_*$. We discuss the plausibility of this hypothesis, as well as its significance for our understanding of Doppler profiles of X-ray emission lines in O stars.Comment: 29 pages, 8 figures, revised version accepted by Ap

Comparing view-based and map-based semantic labelling in real-time SLAM

Generally capable Spatial AI systems must build persistent scene representations where geometric models are combined with meaningful semantic labels. The many approaches to labelling scenes can be divided into two clear groups: view-based which estimate labels from the input view-wise data and then incrementally fuse them into the scene model as it is built; and map-based which label the generated scene model. However, there has so far been no attempt to quantitatively compare view-based and map-based labelling. Here, we present an experimental framework and comparison which uses real-time height map fusion as an accessible platform for a fair comparison, opening up the route to further systematic research in this area

Radiography in high mass X-ray binaries -- Micro-structure of the stellar wind through variability of the column density

In high mass X-ray binaries (HMXBs), an accreting compact object orbits a high mass star which loses mass through a dense and inhomogeneous wind. Using the compact object as an X-ray backlight, the time variability of the absorbing column density in the wind can be exploited in order to shed light on the micro-structure of the wind and obtain unbiased stellar mass loss rates for high mass stars. We explore the impact of clumpiness on the variability of the column density with a simplified wind model. In particular, we focus on the standard deviation of the column density and the characteristic duration of enhanced absorption episodes, and compare them with analytical predictions based on the porosity length. We identified the favorable systems and orbital phases to determine the wind micro-structure. The coherence time scale of the column density is shown to be the self-crossing time of a clump in front of the compact object. We provide a recipe to get accurate measurements of the size and of the mass of the clumps, purely based on the observable time variability of the column density. The coherence time scale grants direct access to the size of the clumps while their mass can be deduced separately from the amplitude of the variability. If it is due to unaccreted passing-by clumps, the high column density variations in some HMXBs requires high mass clumps to reproduce the observed peak-to-peak amplitude and coherence time scales. These clump properties are hardly compatible with the ones derived from first principles. Alternatively, other components could contribute to the variability of the column density: larger orbital scale structures produced by a mechanism still to be identified, or a dense environment in the immediate vicinity of the accretor such as an accretion disk, an outflow or a spherical shell around the magnetosphere of the accreting neutron star

Resonance Scattering In The X-Ray Emission Lines Profiles Of ζ Puppis

We present XMM-Newton Reflection Grating Spectrometer observations of pairs of X-ray emission line profiles from the O star ζ Pup that originate from the same He-like ion. The two profiles in each pair have different shapes and cannot both be consistently fit by models assuming the same wind parameters. We show that the differences in profile shape can be accounted for in a model including the effects of resonance scattering, which affects the resonance line in the pair but not the intercombination line. This implies that resonance scattering is also important in single resonance lines, where its effect is difficult to distinguish from a low effective continuum optical depth in the wind. Thus, resonance scattering may help reconcile X-ray line profile shapes with literature mass-loss rates